Keywords:

SUMMARY

Common variable immunodeficiency (CVID) represents a heterogeneous group of antibody deficiency syndromes, characterized by defective antibody production in which T cell deficiency may play a pathogenic role. A subgroup of CVID patients has impaired in vitro T cell proliferation. Using microarray analyses of T cells from these patients, we found a gene expression pattern different from healthy controls and patients with X-linked agammaglobulinaemia. The profile of the differentially expressed genes suggests enhanced cytotoxic effector functions, antigen experienced or chronically activated T cells and a predominance of CCR7– T cells. Further experiments using flow cytometry revealed a striking predominance of CCR7– T cells in a subgroup of CVID patients, and an association with impaired T cell proliferation. Our observations indicate that a predominance of CCR7– T cells with effector-memory cell features and with reduced proliferative capacity may characterize a subgroup of CVID.

INTRODUCTION

Common variable immunodeficiency (CVID) is a group of immunodeficiency disorders characterized by decreased levels of serum IgG and also often other immunoglobulin classes, as well as recurrent bacterial infections [1]. Although reduced secretion of immunoglobulins by B cells is the hallmark of the disease, a subgroup of patients show signs of T cell abnormalities such as dysregulated cytokine production [e.g. impaired production of interleukin (IL)-2 and IL-10][2,3] and reduced T cell proliferation in vitro[4]. These T cell abnormalities may be of importance both for the defective antibody production and for the clinical manifestations in CVID. In some patients the B cells secrete normal amounts of immunoglobulins when appropriately stimulated in vitro[5,6], suggesting that in some CVID patients a T cell dysfunction leading to inadequate B cell help plays a pathogenic role.

Microarray technology is a powerful tool to obtain a global and unbiased view of gene expression, thus allowing the study of subgroups within disease categories, either as subgroup comparison (comparing predefined subgroups), subgroup prediction (developing markers of predefined subgroups to categorize new samples) or subgroup discovery (defining new, discrete subgroups) [7]. Microarray experiments may also provide insight into biological or pathogenetic mechanisms [8–11]. However, to our knowledge no microarray analyses in CVID have been reported.

To characterize further the T cells in CVID patients with T cell deficiency, we provide a large-scale gene expression profile of T cells from CVID patients with T cell deficiency. We specifically wanted the ‘profile’ to be unbiased of previous selections of any T cell subset, thus providing a set of genes that could then be tested for their association to known or unknown features of human T cells. In additional experiments, we explore further the significance of our gene-expression-based markers of T cell deficiency in a larger cohort of CVID patients.

MATERIALS AND METHODS

Patients

Twenty-three patients with CVID according to the criteria of the International Union of Immunology Societies [1] were included, and tested for impaired T cell proliferation. Of these, six patients with low T cell proliferation (see Results) were selected for further microarray analysis, along with age- and sex-matched healthy controls and five patients with X-linked agammaglobulinaemia (XLA). Characteristics of the CVID patients, XLA patients and healthy controls are shown in Table 1. None of the individuals had any clinically apparent infection when recruited, and the samples were obtained immediately before intravenous or subcutaneous infusions of immunoglobulin. All patients were recruited from our out-patient clinic, and informed consent for blood sampling was obtained from all subjects. The study was conducted according to the ethical guidelines at our hospital and the Helsinki declaration, and was approved by the hospital's authorized representative.

Cell cultures and T cell proliferation assays

The CD3+ T cells were resuspended in RPMI-1640 with 2 mm l-glutamine and 25 m m HEPES buffer (RPMI; Gibco BRL, Paisley, UK) supplemented with 10% fetal calf serum (FCS) and incubated in flat-bottomed 96-well plates (106/ml; 0·2 ml/well; Costar, Cambridge, MA, USA). The cells were stimulated with anti-CD3 (clone SpvT3b; Zymed, San Francisco, CA, USA; final concentration 40 ng/ml), anti-CD28 (clone 15E8; CLB, Amsterdam, the Netherlands; final concentration 50 ng/ml) and cross-linked using immunomagnetic beads coated with sheep antimouse IgG (Dynal) at a cell-to-bead ratio of 1 : 1. For mRNA analyses, cell pellets were harvested after 6 h and stored in liquid nitrogen until further analysis. For proliferation analyses, experiments were performed on freshly isolated T cells in triplicates, and 1 µCi of [3H]thymidine was added to the cell cultures after 48 h, and 16 h later the cultures were harvested onto glass filter strips using an automated multisampler harvester, and analysed subsequently by beta scintillation counting (Scatron, Suffolk, UK). To correct for interassay variation, T cells from an age- and sex-matched healthy control were cultured and analysed with each CVID sample, and proliferation score is reported as ratio of beta scintillation count per minute (cpm) in the CVID sample to cpm in the control sample, except where indicated otherwise.

Flow cytometry and cell sorting

Cryopreserved PBMC were thawed as described previously [12]. Staining was performed using FITC-conjugated anti-CD4 and anti-CD45RA, PE-conjugated anti-TCRγδ and anti-FAS, PerCP-conjugated anti-CD3 and APC-conjugated anti-CD8 and anti-TCRγδ (all from Becton-Dickinson, San Diego, CA, USA) and FITC-conjugated anti-CCR7 (R&D Systems, Oxon, UK). Flow cytometry was performed as described previously [3] using a FACSCalibur instrument with CellQuest software (Becton Dickinson). To estimate possible effects of cryopreservation, the expression of CCR7 on T cells in cryopreserved PBMC was compared to the expression in freshly isolated T cells in preliminary experiments, showing similar proportions of CCR7+ and CCR7– T cells in fresh and thawed cells. Separation of CCR7+ and CCR7– T cell subsets from freshly isolated T cells was performed by staining with PE-conjugated anti-CCR7 (R&D) and using a FACSDiva with FACSDiva software (Becton Dickinson).

Quantification of mRNA was performed using the ABI Prism 7000 (Applied Biosystems, Foster City, CA, USA) [14]. Primers were designed using the Primer Express software version 1·5 (Applied Biosystems) (supplementary Table 1). PCR was performed using qPCR Master Mix for SyBr Green I (Eurogentec, Seraing, Belgium) and 300 n m sense and antisense primers. Gene expression of the housekeeping gene β-actin was used for normalization.

Statistical analyses

Unsupervised hierarchical clustering was performed using cluster and visualized using treeview[15]. Differentially expressed genes were identified by comparing gene expression values from CVID patients with values from XLA patients and healthy controls using ‘statistical analysis of microarrays’ (SAM) [16], with a fold change cut-off set to 1·5. Briefly, SAM calculates the significance of each gene, correcting for multiple testing by permutations of the data, and providing the q-value as a measure of the false discovery rate [17]. We accepted genes with a q < 5% as significantly different between groups. In the permutated comparisons, the t-test with Welsh correction for unequal variances was used. Elsewhere, the two-tailed Mann–Whitney U-test was used when comparing two independent groups; correlations were calculated using Spearman's signed rank test. Tests were considered significant when P < 0·05.

RESULTS

Impaired T cell proliferation in CVID patients

To identify CVID patients with impaired T cell proliferation, we performed proliferation analyses on isolated T cells from 23 CVID patients (median proliferation ratio 65%, range 13–139%). For microarray analyses, we selected six CVID patients with low proliferation ratios (range: 13–63% of proliferation ratio; P = 0·02 comparing proliferation ratio in the remaining 17 CVID patients), along with five XLA patients (who did not have impaired T cell proliferation) and six age- and sex-matched controls. These CVID patients are referred to hereafter as proliferation-deficient CVID (CVIDpd). The absolute proliferation values (cpm) and the proliferation ratios for all individuals included are shown in Table 1.

Gene expression profile distinguishes CVIDpd from XLA and controls

The raw data of the microarray analyses are available at ArrayExpress (http://www.ebi.ac.uk/arrayexpress/, accession number E-MEXP-76). To compare the gene expression patterns obtained in the microarray, we performed a two-dimensional hierarchical clustering analysis [15]. Notably, all arrays from CVIDpd patients clustered separately from those of XLA patients and controls (Fig. 1). In contrast, the arrays from the XLA patients did not cluster separately from healthy controls, indicating that there is no difference in the overall gene expression pattern in T cells between these patients and controls. Confirming this, we found no separate clustering when subjecting only the XLA and control arrays to hierarchic clustering (data not shown). The different clustering pattern in CVIDpd and XLA suggests that the T cell abnormalities in CVIDpd are not secondary to hypogammaglobulinaemia per se, or to the ensuing recurrent bacterial infections or immunoglobulin administration.

Figure 1. CVID clusters separately from XLA and healthy controls. Using cluster software [15], all six data sets from CVID patients (CVID) clustered in one cluster separated from the data sets of the XLA patients (XLA) and the controls (CTR). Each column represents the microarray data from one individual and each row represents a gene. To reduce noise, only genes with a standard deviation >50 across all arrays are included (n = 2690 genes). Up-regulated genes appear in red, down-regulated genes in green.

To validate the results from the hierarchical clustering analyses, we performed permutational analyses [18,19]. While the comparison of the six CVIDpd samples to the remaining samples identified 3089 significantly differentially regulated genes, the number of genes identified when randomly selecting six samples in 100 permutations was significantly lower (mean 1037, 95% confidence interval 962·7–1111·0, P < 0·001), confirming the results of the hierarchical clustering analyses.

Differentially expressed genes in CVIDpd

Having observed the unique clustering of CVIDpd, we next determined which genes had the most differing expression comparing CVIDpd with XLA and healthy controls. Using SAM (see Methods) we identified 197 genes that were overexpressed in CVIDpd, and 309 genes that were underexpressed compared to healthy controls and XLA patients. The entire list of 506 differentially expressed genes is presented as supplementary information (supplementary Table 2). A selection of immunologically relevant genes is shown in Table 2.

Fold change and absolute difference (abs. diff.) comparing mean expression in the CVID samples versus mean expression in the control and XLA samples.

b

q-value is a significance estimate for the proportion (%) of false positive genes, corrected for multiple testing by permutation analyses [16,17]. A q-value = 5% corresponds to P = 0·05.

c

Indicates the response to in vitro activation in microarray experiments by Diehn et al. [23].

Cell cycle-related

Hs.77311

BTG3

1·69

54

3·02

up

Hs.252587

Securin (PTTG1)

1·55

98

0·39

up

Hs.14125

PA26

0·62

−85

0·39

Hs.79070

v-myc

0·47

−302

0·39

up

Hs.106070

p57Kip2

0·41

−75

3·12

Apoptosis-related

Hs.73172

GFI-1

2·28

88

0·39

up

Hs.211600

TNF-α ip3 (A20)

1·98

352

3·02

nc

Hs.82359

Fas

1·56

89

0·39

nc

Hs.159428

Bax

1·55

28

4·93

Hs.79241

Bcl2

0·67

−149

0·39

up

Hs.83429

TRAIL

0·66

−134

3·96

up

Hs.3280

Caspase 6

0·60

−50

0·70

up

Hs.5353

Caspase 10

0·54

−45

0·39

Hs.79428

BNIP3 (Bcl2-ip)

0·54

−54

0·39

nc

Signal transduction

Hs.301746

RAP2A

2·24

350

0·39

Hs.239527

RAP2B

1·94

51

0·39

Hs.74631

CD147 (Basigin)

1·67

80

1·18

Hs.177534

MKP-5 (DUSP10)

1·51

36

1·37

Hs.239818

PI-3-K

0·64

−33

1·37

down

Hs.83428

p105 (NFɛB1)

0·63

−106

0·39

up

Hs.115907

DAGKD

0·63

−102

3·56

down

Hs.29877

Txk

0·55

−97

1·37

down

Hs.348

CAMK4

0·51

−55

0·95

down

Hs.80395

MAL

0·50

−342

0·39

down

Hs.121128

BRDG1 (STAP1)

0·50

−25

3·02

Hs.44865

LEF-1

0·47

−727

0·39

cAMP-associated

Hs.301946

AKAP13

1·59

141

2·60

Hs.42322

AKAP2

1·52

48

3·02

nc

Hs.433700

PKIA

0·66

−38

0·95

Hs.78746

PDE8A

0·48

−58

0·39

Hs.337616

PDE3B

0·45

−89

0·39

Cell-to-cell communication

Hs.856

IFN-γ

3·11

106

0·39

up

Hs.74011

LAG-3

2·13

77

0·39

up

Hs.342874

TGFβR3

1·67

98

4·46

down

Hs.814

HLA-DP b1

1·66

623

3·02

down

Hs.272409

T-box 21 (T-bet)

1·62

219

3·02

up

Hs.2259

CD3γ

1·54

171

0·39

down

Hs.181097

OX-40 l

0·65

−40

3·56

nc

Hs.75545

IL-4R

0·61

−96

0·39

nc

Hs.64310

IL-11Rα

0·61

−75

0·39

down

Hs.232068

ZEB-1

0·61

−47

0·95

nc

Hs.82065

gp130 (IL-6Rβ)

0·61

−26

3·96

nc

Hs.193400

IL-6Rα (CD126)

0·60

−60

2·10

Hs.428

Flt-3LG

0·59

−55

0·39

down

Hs.890

LTβ

0·58

−857

3·02

nc

Hs.1369

DAF (CD55)

0·53

−122

0·39

nc

Hs.154299

Thrombin-RL

0·50

−32

0·39

down

Hs.289019

LTBP3

0·46

−71

3·02

Adhesion/migration

Hs.75703

MIP-1β (CCL4)

1·96

294

0·70

up

Hs.241392

RANTES (CCL5)

1·70

919

0·39

down

Hs.75626

LFA-3 (CD58)

1·56

58

0·39

Hs.78913

CX3CR1

1·55

928

1·53

down

Hs.137548

CD84

1·52

102

4·93

Hs.433303

ICAM-2

0·58

−388

0·39

down

Hs.1652

CCR7

0·39

−753

0·39

down

Hs.227730

Integrin α6

0·37

−195

0·39

down

TCRγδ-associated

Hs.274509

TCRγ c2

2·81

956

0·39

nc

Hs.382367

Galectin-1

1·93

511

0·39

Hs.112259

TCRγ v9

1·89

995

0·39

nc

Hs.87450

Cathepsin W

1·80

389

3·12

nc

Hs.268531

Granzyme M

1·60

125

3·56

Hs.73895

4–1BB

1·60

21

3·56

up

Hs.54457

CD81

1·57

301

0·39

Hs.74647

TCRα

0·62

−26

2·10

nc

Cytotoxic T cell-associated

Hs.348264

Granzyme H

2·87

1629

0·39

Hs.132906

CRACC

2·01

95

0·39

Hs.10306

NKG7 (GMP-17)

2·00

1638

0·39

Hs.41682

NKG2A (CD159A)

1·95

431

0·39

Hs.85258

CD8α

1·88

664

0·39

nc

Hs.146322

KLRG1

1·81

314

1·53

down

Hs.90708

Granzyme A

1·68

724

0·95

nc

Hs.105806

Granulysin

1·64

922

2·10

Hs.81743

CD160 (BY55)

1·63

123

3·56

Hs.1051

Granzyme B

1·61

547

3·02

up

Hs.157872

2B4 (CD244)

1·57

68

2·60

Hs.411106

Perforin

1·52

1146

0·70

Although the CVIDpd patients included in the microarray analyses were selected by demonstration of impaired T cell proliferative capacity, we noticed that the proportion of CD4+ T cells of the CVIDpd patients included was about 1·5-fold lower than in the XLA patients and healthy controls. However, when comparing the in vitro proliferation in a larger cohort of CVID patients (n = 23) to the CD4/CD8 T cell ratios, we found no significant correlation (r = 0·10, P = 0·64), indicating that the decreased T cell proliferation does not merely reflect a decreased CD4/CD8 T cell ratio. Moreover, we set the minimum fold difference between CVID and XLA/healthy controls to 1·5 in the SAM analysis (see Methods), thus ensuring that that the differences detected exceed the differences caused by skewed CD4/CD8 T cell ratios in the CVID patients.

Additionally, we used flow cytometry to analyse the expression of certain markers discovered in the microarray analyses on CD4+ and CD8+ T cell subsets. First, flow cytometric analyses supported the microarray observations concerning the expression of Fas, showing markedly higher surface expression of Fas on CD4+ and CD8+ T cell subsets in CVIDpd comparing XLA and healthy controls (Fig. 2a). Secondly, a case report has previously described increased proportions of γδ-T cells in CVID [20], and the microarray analyses indicated many genes related to the γδ-phenotype of T cells to be overexpressed in CVIDpd (Table 2). Flow cytometric analyses of T cells from the individuals included in the microarray confirm an increased proportion of γδ-T cells in five of the six CVIDpd patients examined (Fig. 2b). Moreover, in patients and controls, most of the γδ-T cells were CD4–CD8–. Finally, T cells lacking the lymph node homing receptor CCR7 have been described previously as ‘effector-memory’ T cells [21] and, notably, the gene expression of CCR7 was significantly decreased in CVIDpd comparing XLA and healthy controls. Again, using flow cytometry, we found a striking reduction of the CCR7+ T cell subset in CVIDpd compared to healthy controls and XLA patients (11 ± 3·9% in CVIDpdversus 37 ± 8·1% in controls, P = 0·02). Importantly, the predominance of CCR7– T cells in CVIDpd was evident in both CD4+ and CD8+ T cell subsets (Fig. 2c).

Figure 2. Increased proportion of Fashigh and TCRδ+ and decreased proportion of CCR7+ expressing T cells in CVIDpd. Left graphs show individual expression values as percentage of CD3+ cells for CVIDpd patients, XLA patients and controls. Please note varying scale on the y-axes. Middle and right graphs show the expression on CD3+ cells of Fas (a), TCRγδ(b) and CCR7 related to expression of CD8 in a representative CVIDpd patient (middle) and a healthy control (right). Figures in dot-blots indicate percentage positive of all T cells. (*)P = 0·06; *P = 0·02; **P = 0·002.

Gene expression in CVIDpd related to T cell activation

In vivo‘preactivation’ has previously been suggested to characterize T cells from CVID patients [22]. When comparing our results to those from a microarray performed on in vitro activated primary human T cells [23], we found a number of similarities between the expression profile of in vitro activated T cells and circulating T cells from CVIDpd patients (Table 2), suggesting that the gene expression pattern in CVIDpd could, at least in part, reflect in vivo activation.

To elucidate further this issue, we measured the expression of ‘activation-like’ genes using real-time RT-PCR after stimulating T cells from the individuals included in the microarray analyses with anti-CD3 and anti-CD28. Several distinct patterns were revealed (Fig. 3). First, the gene expression of IL-11Rα, integrin α6 and CCR7, genes that were down-regulated in CVIDpd in our microarray analyses and in in vitro activated T cells [23], were further decreased upon in vitro stimulation in CVIDpd patients (Fig. 3a). Secondly, the gene expression of interferon (IFN)-γ, granzyme B and MIP-1β, genes with increased expression in CVIDpd in our microarray analyses and in vitro activated T cells [23], was even higher after in vitro stimulation, with more pronounced effects in CVIDpd patients than in controls (Fig. 3b). Thus, the preactivation pattern seems to be amplified upon in vitro activation, with further down-regulation of underexpressed genes and further enhancement of overexpressed genes. In contrast, some genes in our microarray showed a differential expression opposite to the ‘activation-like’ profile; specifically, Bcl-2 was under-expressed and RANTES and CX3CR1 were over-expressed in CVIDpd. Albeit dissimilar to the expression pattern of in vitro activated T cells [23], this pattern has been observed previously in T cells subjected to persistent in vivo stimulation [24–26]. Interestingly, upon in vitro activation the expression of these genes changed towards the normal (Bcl-2 expression increased and RANTES and CX3CR1 decreased) (Fig. 3c). Taken together, these observations indicate that the gene profile of CVIDpd may reflect persistent in vivo activation of T cells.

Figure 3. Transcriptional response to in vitro activation of selected genes. (a and b) Genes with an ‘activation-like’ profile (see Table 2). (a) Genes that were underexpressed (IL-11Rα, integrin α6, CCR7) and (b) genes that were overexpressed (IFN-γ, granzyme B, MIP-1β) in the microarray analysis. (c) Genes expressed differentially in proliferation-deficient CVID in the microarray analysis, but not with an ‘activation-like’ pattern (RANTES, CX3CR1, Bcl-2). Graphs show response to in vitro stimulation assessed by real-time quantitative RT-PCR relative to the expression of β-actin mRNA. –, unstimulated; +, αCD3/αCD28 stimulated.

Both the similarities to persistently in vivo activated T cells and the features of effector cells observed in the gene expression of CVIDpd may reflect a predominance of the CCR7– T cell subset termed effector-memory T cells [21] in CVIDpd. To study whether the reduced CCR7 expression may be considered a marker of impaired T cell proliferation in CVID, we next analysed the expression of CCR7 on peripheral blood T cells on all CVID patients. Strikingly, in the CVID group as a whole (n = 23), we found strongly reduced proportions of CCR7+ T cells compared with controls [median (25–75 percentile) CCR7+ T cells: 15% (11·0–32·5) in CVID versus 50% (28·5–70·0) in controls, P < 0·001, Fig. 4a]. However, the proportion of CCR7+ T cells was within the control ranges in 9/23 patients, illustrating the heterogeneity in CVID. Notably, the proliferation was significantly lower in CVID patients with predominance of CCR7– T cells comparing the patients having proportions of CCR7– T cells within the control ranges (Fig. 4b), and there was a significant positive correlation between the proportion of CCR7+ T cells and in vitro T cell proliferation in CVID patients (Fig. 4c). Moreover, isolated CCR7– T cells from three healthy controls showed a markedly decreased (∼1/30) proliferation compared to the CCR7+ T cell subset (Fig. 4d), further indicating a relationship between the expression of CCR7 and T cell proliferation. Taken together, these observations indicate a link between the predominance of CCR7– T cells and the reduced proliferative capacity in subgroups of CVID.

DISCUSSION

There is considerable heterogeneity in the CVID population, and efforts have been made to define discrete subgroups of CVID [27,28], ultimately to elucidate yet-unknown pathogenetic mechanisms in CVID. In many CVID patients impaired T cell function has been observed [4], often assessed using in vitro thymidine incorporation in T cells. We wished to focus on this T cell pathology in CVID, and when using high-throughput microarray analyses and hierarchical clustering we found that samples from CVID patients with impaired T cell proliferation cluster separately from healthy controls, while XLA patients cluster together with the healthy controls. The gene expression profile of T cells from CVID patients with impaired T cell proliferation suggests a predominance of T cells with enhanced cytotoxic effector functions and possibly characteristics of persistently in vivo activated T cells.

There was a striking predominance of CCR7– T cells in the CVID patients included in the microarray analysis, evident on both CD4+ and CD8+ T cells. The chemokine receptor CCR7 is normally expressed on mature dendritic cells, B cells and naive T cells, and mediates migration to secondary lymphoid organs [29]. It has been suggested previously that the CCR7– T cell subset defines an antigen-experienced, tissue-homing (not lymph-node homing) memory T cell population with reduced proliferative capacity [21,30,31]. These T cells have been termed effector-memory T cells, as increased effector functions have also been described in CCR7– T cells [21,32], such as a strong expression of IFN-γ, perforin, granzyme A and GMP-17. Notably, we found that the gene expression of all these and several other effector molecules was increased in T cells from CVID patients with impaired T cell proliferation. Using flow cytometry, we demonstrated that increased proportions of CCR7– T cells correlate to the T cell proliferative capacity, suggesting that increased proportions of CCR7– T cells may be a marker of a subgroup of CVID patients with impaired T cell proliferative capacity. Moreover, it is likely that the increased proportion of CCR7– contributed to the gene expression profile of T cells from CVIDpd patients presented in this study.

Previous reports on T cell function in CVID have indicated reduced IL-2 secretion and reduced expression of CD40L in some CVID patients [33,34]. Interestingly, both have been reported to be reduced in CCR7– T cells [21,32]. It is therefore conceivable that the reduced co-stimulatory capacity of CCR7– T cells may contribute to the impaired T–B cell interaction, contributing to the hypogammaglobulinaemia in a subgroup of CVID.

The reason for the predominance of CCR7– T cells in a possible subgroup of CVID patients is not clear. However, in CVID patients with impaired T cell proliferation, the gene expression profile suggests a persistent in vivo activation, possibly leading to the accumulation of CCR7– effector memory T cells. Additionally, antigen experienced or memory T cells are associated with several other characteristics of the gene profile in the CVID patients studied in this microarray analysis, i.e. the elevated expression of RANTES [35], CX3CR1 [26], KLRG1 [36] and NKG2A [37] and the reduced expression of Bcl-2 [31] and IL-6R [38]. Thus, it is possible that the predominance of CCR7– T cells in CVID demonstrated in this study may reflect a chronic antigen stimulation in vivo in CVID. Chronic viral infections have been suggested previously to play a role in CVID [39,40] and interestingly, a predominance of CCR7– effector memory T cells has also been observed among virus-specific T cells in several chronic viral infections [30,32].

A recent report by Yamashita et al. describes a predominance of CCR7– cells among CD4+ T cells in patients with chronic graft-versus-host-disease (cGVHD) [41]. cGVHD is associated with chronic antigenic stimulation, and intriguingly, some patients with cGVHD may present with hypogammaglobulinaemia [42]. Thus, whether similar mechanisms are operative in CVID patients with impaired T cell proliferation and in chronic virus infections or cGVHD remains to be demonstrated. Nevertheless, the observations of Yamashita et al., together with the findings in the present study, suggest a potential role for dysregulated CCR7 expression in the pathogenesis of human disease.

Most of the CVID patients included in the microarray analysis had a reduced CD4/CD8 T cell ratio, and it could be argued that the differences in the gene expression reflects this difference in primarily T cell subset distribution. However, the differences in expression of genes included in the gene expression profile reported in this study exceed the differences in proportions of CD4 or CD8 T cells. Thus, the differences between CVID and controls were evident in both CD4+ and CD8+ T cells when analysed on the protein level using flow cytometry. This suggests that the differences between CVIDpd and the two control groups (i.e. XLA patients and healthy controls) do not merely reflect skewed proportions of CD4+ and CD8+ T cells in CVID. Nevertheless, future studies analysing the gene expression profile in purified CD4+ or CD8+ T cells from CVID patients would provide additional valuable information about T cell pathology in CVID.

In conclusion, we have been able to demonstrate that CVID patients with impaired T cell proliferation are characterized by a gene expression profile possibly reflecting in vivo activation and enhanced cytotoxic effector functions. These CVID patients manifest a predominance of CCR7– T cells, which is related to impaired T cell proliferation in CVID. Further work is necessary to study the reason for the predominance of CCR7– T cells in these patients and the possible functional consequences in CVID.